Project description:Dysregulated miRNA in human colorectal cancer (CRC) were identified through comparison between 4 CRC tumors and their adjacent normal tissues by miRNA array. Histologically-confirmed CRC were included in this study. CRC tissues and paired adjacent normal tissues were obtained from the resected surgical specimens. The adjacent normal tissue is composed of normal colonic mucosa located at approximately 10 cm away from the cancer tissue. miRNA profiling of 754 human miRNAs was performed using TaqMan Human MiRNA Array Set v3.0. Quantitative real-time polymerase chain reaction (Q-PCR) was performed using Applied Biosystems 7900HT Real-Time PCR System (Applied Biosystems). Results were analyzed by the SDS RQ Manager 1.2 software (Applied Biosystems).
Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling
Project description:Purpose: provide evidence that RNA-seq can add information to transcriptome profiling already discovered by other technologies for atopic dermatitis Methods: mRNA profiles of 20 atopic dermatitis were analyzed to compare lesional and non-lesional skin, then transcriptomes found by reads were compared to Microarray and RT-PCR Results:RNA-seq provided complementary genes to AD transcriptome IL-36 and TREM-1 Conclusions: Our study represents the first analysis of lesional AD tissue by RNA-seq and comparison to microarray and RT-PCR
Project description:We hypothesize that there is a gene signature which will improve our ability to predict development of metastatic disease in STS patients. The objective of this study was to determine the feasibility of using cDNA microarray and quantitative real-time PCR (qRT-PCR) analysis to determine gene expression patterns in metastatic versus non-metastatic canine STSs, given the inherent heterogeneity of this group of tumors. Five STSs from dogs with metastatic disease were evaluated in comparison to eight STSs from dogs without metastasis. Tumor RNA was extracted, processed and labeled for application to the Affymetrix Canine Genechip 2.0 Array. Array fluorescence was normalized using D-Chip software and data analysis was performed with JMP/Genomics. Differential gene expression was validated using qRT-PCR. Over 200 genes were differentially expressed at a false discovery rate of 5%. Differential gene expression was validated for five genes upregulated in metastatic tumors. Quantitative RT-PCR confirmed increased relative expression of all five genes of interest in the metastatic STSs. Our results demonstrate that microarray and qRT-PCR are feasible methods for comparing gene signatures in canine STSs. Further evaluation of differences in gene expression between metastatic and non-metastatic STSs is likely to identify genes important in the development of metastatic disease and improve our ability to prognosticate for individual patients.